# coding=utf-8
# Copyright 2018 The Google AI Team Authors.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# Lint as: python2, python3
"""Utility functions for SQuAD v1.1/v2.0 datasets."""

from __future__ import absolute_import
from __future__ import division

# from __future__ import google_type_annotations
from __future__ import print_function
import collections
import json
import math
import re
import string
import sys
import numpy as np
import six
from six.moves import map
from six.moves import range
import tensorflow.compat.v1 as tf
import tokenization
from tensorflow.contrib import data as contrib_data
from tensorflow.contrib import layers as contrib_layers
from tensorflow.contrib import tpu as contrib_tpu
from tqdm import tqdm

_PrelimPrediction = collections.namedtuple(  # pylint: disable=invalid-name
    'PrelimPrediction',
    [
        'feature_index',
        'start_index',
        'end_index',
        'start_log_prob',
        'end_log_prob',
    ],
)

_NbestPrediction = collections.namedtuple(  # pylint: disable=invalid-name
    'NbestPrediction', ['text', 'start_log_prob', 'end_log_prob']
)

RawResult = collections.namedtuple(
    'RawResult', ['unique_id', 'start_log_prob', 'end_log_prob']
)

RawResultV2 = collections.namedtuple(
    'RawResultV2',
    [
        'unique_id',
        'start_top_log_probs',
        'start_top_index',
        'end_top_log_probs',
        'end_top_index',
        'cls_logits',
    ],
)


class SquadExample(object):
    """A single training/test example for simple sequence classification.

     For examples without an answer, the start and end position are -1.
  """

    def __init__(
        self,
        qas_id,
        question_text,
        paragraph_text,
        orig_answer_text = None,
        start_position = None,
        end_position = None,
        is_impossible = False,
    ):
        self.qas_id = qas_id
        self.question_text = question_text
        self.paragraph_text = paragraph_text
        self.orig_answer_text = orig_answer_text
        self.start_position = start_position
        self.end_position = end_position
        self.is_impossible = is_impossible

    def __str__(self):
        return self.__dict__


class InputFeatures(object):
    """A single set of features of data."""

    def __init__(
        self,
        unique_id,
        example_index,
        doc_span_index,
        tok_start_to_orig_index,
        tok_end_to_orig_index,
        token_is_max_context,
        tokens,
        input_ids,
        input_mask,
        segment_ids,
        paragraph_len,
        p_mask = None,
        start_position = None,
        end_position = None,
        is_impossible = None,
    ):
        self.unique_id = unique_id
        self.example_index = example_index
        self.doc_span_index = doc_span_index
        self.tok_start_to_orig_index = tok_start_to_orig_index
        self.tok_end_to_orig_index = tok_end_to_orig_index
        self.token_is_max_context = token_is_max_context
        self.tokens = tokens
        self.input_ids = input_ids
        self.input_mask = input_mask
        self.segment_ids = segment_ids
        self.paragraph_len = paragraph_len
        self.start_position = start_position
        self.end_position = end_position
        self.is_impossible = is_impossible
        self.p_mask = p_mask


def read_squad_examples(input_file, is_training):
    """Read a SQuAD json file into a list of SquadExample."""
    with tf.gfile.Open(input_file, 'r') as reader:
        input_data = json.load(reader)['data']

    examples = []
    for entry in tqdm(input_data):
        for paragraph in entry['paragraphs']:
            paragraph_text = paragraph['context']

            for qa in paragraph['qas']:
                qas_id = qa['id']
                question_text = qa['question']
                start_position = None
                orig_answer_text = None
                is_impossible = False

                if is_training:
                    is_impossible = qa.get('is_impossible', False)
                    if (len(qa['answers']) != 1) and (not is_impossible):
                        raise ValueError(
                            'For training, each question should have exactly 1 answer.'
                        )
                    if not is_impossible:
                        answer = qa['answers'][0]
                        orig_answer_text = answer['text']
                        start_position = answer['answer_start']
                    else:
                        start_position = -1
                        orig_answer_text = ''

                example = SquadExample(
                    qas_id = qas_id,
                    question_text = question_text,
                    paragraph_text = paragraph_text,
                    orig_answer_text = orig_answer_text,
                    start_position = start_position,
                    is_impossible = is_impossible,
                )
                examples.append(example)

    return examples


def _convert_index(index, pos, m = None, is_start = True):
    """Converts index."""
    if index[pos] is not None:
        return index[pos]
    n = len(index)
    rear = pos
    while rear < n - 1 and index[rear] is None:
        rear += 1
    front = pos
    while front > 0 and index[front] is None:
        front -= 1
    assert index[front] is not None or index[rear] is not None
    if index[front] is None:
        if index[rear] >= 1:
            if is_start:
                return 0
            else:
                return index[rear] - 1
        return index[rear]
    if index[rear] is None:
        if m is not None and index[front] < m - 1:
            if is_start:
                return index[front] + 1
            else:
                return m - 1
        return index[front]
    if is_start:
        if index[rear] > index[front] + 1:
            return index[front] + 1
        else:
            return index[rear]
    else:
        if index[rear] > index[front] + 1:
            return index[rear] - 1
        else:
            return index[front]


def convert_examples_to_features(
    examples,
    tokenizer,
    max_seq_length,
    doc_stride,
    max_query_length,
    is_training,
    do_lower_case,
):
    """Loads a data file into a list of `InputBatch`s."""

    cnt_pos, cnt_neg = 0, 0
    unique_id = 1000000000
    max_n, max_m = 1024, 1024
    f = np.zeros((max_n, max_m), dtype = np.float32)
    features = []

    for n in tqdm(range(len(examples))):
        example_index = n
        example = examples[n]

        if example_index % 100 == 0:
            tf.logging.info(
                'Converting {}/{} pos {} neg {}'.format(
                    example_index, len(examples), cnt_pos, cnt_neg
                )
            )

        query_tokens = tokenization.encode_ids(
            tokenizer.sp_model,
            tokenization.preprocess_text(
                example.question_text, lower = do_lower_case
            ),
        )

        if len(query_tokens) > max_query_length:
            query_tokens = query_tokens[0:max_query_length]

        paragraph_text = example.paragraph_text
        para_tokens = tokenization.encode_pieces(
            tokenizer.sp_model,
            tokenization.preprocess_text(
                example.paragraph_text, lower = do_lower_case
            ),
            return_unicode = False,
        )

        chartok_to_tok_index = []
        tok_start_to_chartok_index = []
        tok_end_to_chartok_index = []
        char_cnt = 0
        para_tokens = [six.ensure_text(token, 'utf-8') for token in para_tokens]
        for i, token in enumerate(para_tokens):
            new_token = six.ensure_text(token).replace(
                tokenization.SPIECE_UNDERLINE.decode('utf-8'), ' '
            )
            chartok_to_tok_index.extend([i] * len(new_token))
            tok_start_to_chartok_index.append(char_cnt)
            char_cnt += len(new_token)
            tok_end_to_chartok_index.append(char_cnt - 1)

        tok_cat_text = ''.join(para_tokens).replace(
            tokenization.SPIECE_UNDERLINE.decode('utf-8'), ' '
        )
        n, m = len(paragraph_text), len(tok_cat_text)

        if n > max_n or m > max_m:
            max_n = max(n, max_n)
            max_m = max(m, max_m)
            f = np.zeros((max_n, max_m), dtype = np.float32)

        g = {}

        def _lcs_match(max_dist, n = n, m = m):
            """Longest-common-substring algorithm."""
            f.fill(0)
            g.clear()

            ### longest common sub sequence
            # f[i, j] = max(f[i - 1, j], f[i, j - 1], f[i - 1, j - 1] + match(i, j))
            for i in range(n):

                # note(zhiliny):
                # unlike standard LCS, this is specifically optimized for the setting
                # because the mismatch between sentence pieces and original text will
                # be small
                for j in range(i - max_dist, i + max_dist):
                    if j >= m or j < 0:
                        continue

                    if i > 0:
                        g[(i, j)] = 0
                        f[i, j] = f[i - 1, j]

                    if j > 0 and f[i, j - 1] > f[i, j]:
                        g[(i, j)] = 1
                        f[i, j] = f[i, j - 1]

                    f_prev = f[i - 1, j - 1] if i > 0 and j > 0 else 0
                    if (
                        tokenization.preprocess_text(
                            paragraph_text[i],
                            lower = do_lower_case,
                            remove_space = False,
                        )
                        == tok_cat_text[j]
                        and f_prev + 1 > f[i, j]
                    ):
                        g[(i, j)] = 2
                        f[i, j] = f_prev + 1

        max_dist = abs(n - m) + 5
        for _ in range(2):
            _lcs_match(max_dist)
            if f[n - 1, m - 1] > 0.8 * n:
                break
            max_dist *= 2

        orig_to_chartok_index = [None] * n
        chartok_to_orig_index = [None] * m
        i, j = n - 1, m - 1
        while i >= 0 and j >= 0:
            if (i, j) not in g:
                break
            if g[(i, j)] == 2:
                orig_to_chartok_index[i] = j
                chartok_to_orig_index[j] = i
                i, j = i - 1, j - 1
            elif g[(i, j)] == 1:
                j = j - 1
            else:
                i = i - 1

        if (
            all(v is None for v in orig_to_chartok_index)
            or f[n - 1, m - 1] < 0.8 * n
        ):
            tf.logging.info('MISMATCH DETECTED!')
            continue

        tok_start_to_orig_index = []
        tok_end_to_orig_index = []
        for i in range(len(para_tokens)):
            start_chartok_pos = tok_start_to_chartok_index[i]
            end_chartok_pos = tok_end_to_chartok_index[i]
            start_orig_pos = _convert_index(
                chartok_to_orig_index, start_chartok_pos, n, is_start = True
            )
            end_orig_pos = _convert_index(
                chartok_to_orig_index, end_chartok_pos, n, is_start = False
            )

            tok_start_to_orig_index.append(start_orig_pos)
            tok_end_to_orig_index.append(end_orig_pos)

        if not is_training:
            tok_start_position = tok_end_position = None

        if is_training and example.is_impossible:
            tok_start_position = 0
            tok_end_position = 0

        if is_training and not example.is_impossible:
            start_position = example.start_position
            end_position = start_position + len(example.orig_answer_text) - 1

            start_chartok_pos = _convert_index(
                orig_to_chartok_index, start_position, is_start = True
            )
            tok_start_position = chartok_to_tok_index[start_chartok_pos]

            end_chartok_pos = _convert_index(
                orig_to_chartok_index, end_position, is_start = False
            )
            tok_end_position = chartok_to_tok_index[end_chartok_pos]
            assert tok_start_position <= tok_end_position

        def _piece_to_id(x):
            if six.PY2 and isinstance(x, six.text_type):
                x = six.ensure_binary(x, 'utf-8')
            return tokenizer.sp_model.PieceToId(x)

        all_doc_tokens = list(map(_piece_to_id, para_tokens))

        # The -3 accounts for [CLS], [SEP] and [SEP]
        max_tokens_for_doc = max_seq_length - len(query_tokens) - 3

        # We can have documents that are longer than the maximum sequence length.
        # To deal with this we do a sliding window approach, where we take chunks
        # of the up to our max length with a stride of `doc_stride`.
        _DocSpan = collections.namedtuple(  # pylint: disable=invalid-name
            'DocSpan', ['start', 'length']
        )
        doc_spans = []
        start_offset = 0
        while start_offset < len(all_doc_tokens):
            length = len(all_doc_tokens) - start_offset
            if length > max_tokens_for_doc:
                length = max_tokens_for_doc
            doc_spans.append(_DocSpan(start = start_offset, length = length))
            if start_offset + length == len(all_doc_tokens):
                break
            start_offset += min(length, doc_stride)

        for (doc_span_index, doc_span) in enumerate(doc_spans):
            tokens = []
            token_is_max_context = {}
            segment_ids = []
            p_mask = []

            cur_tok_start_to_orig_index = []
            cur_tok_end_to_orig_index = []

            tokens.append(tokenizer.sp_model.PieceToId('[CLS]'))
            segment_ids.append(0)
            p_mask.append(0)
            for token in query_tokens:
                tokens.append(token)
                segment_ids.append(0)
                p_mask.append(1)
            tokens.append(tokenizer.sp_model.PieceToId('[SEP]'))
            segment_ids.append(0)
            p_mask.append(1)

            for i in range(doc_span.length):
                split_token_index = doc_span.start + i

                cur_tok_start_to_orig_index.append(
                    tok_start_to_orig_index[split_token_index]
                )
                cur_tok_end_to_orig_index.append(
                    tok_end_to_orig_index[split_token_index]
                )

                is_max_context = _check_is_max_context(
                    doc_spans, doc_span_index, split_token_index
                )
                token_is_max_context[len(tokens)] = is_max_context
                tokens.append(all_doc_tokens[split_token_index])
                segment_ids.append(1)
                p_mask.append(0)
            tokens.append(tokenizer.sp_model.PieceToId('[SEP]'))
            segment_ids.append(1)
            p_mask.append(1)

            paragraph_len = len(tokens)
            input_ids = tokens

            # The mask has 1 for real tokens and 0 for padding tokens. Only real
            # tokens are attended to.
            input_mask = [1] * len(input_ids)

            # Zero-pad up to the sequence length.
            while len(input_ids) < max_seq_length:
                input_ids.append(0)
                input_mask.append(0)
                segment_ids.append(0)
                p_mask.append(1)

            assert len(input_ids) == max_seq_length
            assert len(input_mask) == max_seq_length
            assert len(segment_ids) == max_seq_length

            span_is_impossible = example.is_impossible
            start_position = None
            end_position = None
            if is_training and not span_is_impossible:
                # For training, if our document chunk does not contain an annotation
                # we throw it out, since there is nothing to predict.
                doc_start = doc_span.start
                doc_end = doc_span.start + doc_span.length - 1
                out_of_span = False
                if not (
                    tok_start_position >= doc_start
                    and tok_end_position <= doc_end
                ):
                    out_of_span = True
                if out_of_span:
                    # continue
                    start_position = 0
                    end_position = 0
                    span_is_impossible = True
                else:
                    doc_offset = len(query_tokens) + 2
                    start_position = tok_start_position - doc_start + doc_offset
                    end_position = tok_end_position - doc_start + doc_offset

            if is_training and span_is_impossible:
                start_position = 0
                end_position = 0

            if example_index < 20:
                tf.logging.info('*** Example ***')
                tf.logging.info('unique_id: %s' % (unique_id))
                tf.logging.info('example_index: %s' % (example_index))
                tf.logging.info('doc_span_index: %s' % (doc_span_index))
                tf.logging.info(
                    'tok_start_to_orig_index: %s'
                    % ' '.join([str(x) for x in cur_tok_start_to_orig_index])
                )
                tf.logging.info(
                    'tok_end_to_orig_index: %s'
                    % ' '.join([str(x) for x in cur_tok_end_to_orig_index])
                )
                tf.logging.info(
                    'token_is_max_context: %s'
                    % ' '.join(
                        [
                            '%d:%s' % (x, y)
                            for (x, y) in six.iteritems(token_is_max_context)
                        ]
                    )
                )
                tf.logging.info(
                    'input_pieces: %s'
                    % ' '.join(
                        [tokenizer.sp_model.IdToPiece(x) for x in tokens]
                    )
                )
                tf.logging.info(
                    'input_ids: %s' % ' '.join([str(x) for x in input_ids])
                )
                tf.logging.info(
                    'input_mask: %s' % ' '.join([str(x) for x in input_mask])
                )
                tf.logging.info(
                    'segment_ids: %s' % ' '.join([str(x) for x in segment_ids])
                )

                if is_training and span_is_impossible:
                    tf.logging.info('impossible example span')

                if is_training and not span_is_impossible:
                    pieces = [
                        tokenizer.sp_model.IdToPiece(token)
                        for token in tokens[start_position : (end_position + 1)]
                    ]
                    answer_text = tokenizer.sp_model.DecodePieces(pieces)
                    tf.logging.info('start_position: %d' % (start_position))
                    tf.logging.info('end_position: %d' % (end_position))
                    tf.logging.info(
                        'answer: %s'
                        % (tokenization.printable_text(answer_text))
                    )

                    # note(zhiliny): With multi processing,
                    # the example_index is actually the index within the current process
                    # therefore we use example_index=None to avoid being used in the future.
                    # The current code does not use example_index of training data.
            if is_training:
                feat_example_index = None
            else:
                feat_example_index = example_index

            feature = InputFeatures(
                unique_id = unique_id,
                example_index = feat_example_index,
                doc_span_index = doc_span_index,
                tok_start_to_orig_index = cur_tok_start_to_orig_index,
                tok_end_to_orig_index = cur_tok_end_to_orig_index,
                token_is_max_context = token_is_max_context,
                tokens = [tokenizer.sp_model.IdToPiece(x) for x in tokens],
                input_ids = input_ids,
                input_mask = input_mask,
                segment_ids = segment_ids,
                paragraph_len = paragraph_len,
                start_position = start_position,
                end_position = end_position,
                is_impossible = span_is_impossible,
                p_mask = p_mask,
            )

            features.append(feature)

            unique_id += 1
            if span_is_impossible:
                cnt_neg += 1
            else:
                cnt_pos += 1

    tf.logging.info(
        'Total number of instances: {} = pos {} neg {}'.format(
            cnt_pos + cnt_neg, cnt_pos, cnt_neg
        )
    )
    return features


def _check_is_max_context(doc_spans, cur_span_index, position):
    """Check if this is the 'max context' doc span for the token."""

    # Because of the sliding window approach taken to scoring documents, a single
    # token can appear in multiple documents. E.g.
    #  Doc: the man went to the store and bought a gallon of milk
    #  Span A: the man went to the
    #  Span B: to the store and bought
    #  Span C: and bought a gallon of
    #  ...
    #
    # Now the word 'bought' will have two scores from spans B and C. We only
    # want to consider the score with "maximum context", which we define as
    # the *minimum* of its left and right context (the *sum* of left and
    # right context will always be the same, of course).
    #
    # In the example the maximum context for 'bought' would be span C since
    # it has 1 left context and 3 right context, while span B has 4 left context
    # and 0 right context.
    best_score = None
    best_span_index = None
    for (span_index, doc_span) in enumerate(doc_spans):
        end = doc_span.start + doc_span.length - 1
        if position < doc_span.start:
            continue
        if position > end:
            continue
        num_left_context = position - doc_span.start
        num_right_context = end - position
        score = (
            min(num_left_context, num_right_context) + 0.01 * doc_span.length
        )
        if best_score is None or score > best_score:
            best_score = score
            best_span_index = span_index

    return cur_span_index == best_span_index


def _get_best_indexes(logits, n_best_size):
    """Get the n-best logits from a list."""
    index_and_score = sorted(
        enumerate(logits), key = lambda x: x[1], reverse = True
    )

    best_indexes = []
    for i in range(len(index_and_score)):
        if i >= n_best_size:
            break
        best_indexes.append(index_and_score[i][0])
    return best_indexes


def _compute_softmax(scores):
    """Compute softmax probability over raw logits."""
    if not scores:
        return []

    max_score = None
    for score in scores:
        if max_score is None or score > max_score:
            max_score = score

    exp_scores = []
    total_sum = 0.0
    for score in scores:
        x = math.exp(score - max_score)
        exp_scores.append(x)
        total_sum += x

    probs = []
    for score in exp_scores:
        probs.append(score / total_sum)
    return probs


class FeatureWriter(object):
    """Writes InputFeature to TF example file."""

    def __init__(self, filename, is_training):
        self.filename = filename
        self.is_training = is_training
        self.num_features = 0
        self._writer = tf.python_io.TFRecordWriter(filename)

    def process_feature(self, feature):
        """Write a InputFeature to the TFRecordWriter as a tf.train.Example."""
        self.num_features += 1

        def create_int_feature(values):
            feature = tf.train.Feature(
                int64_list = tf.train.Int64List(value = list(values))
            )
            return feature

        features = collections.OrderedDict()
        features['unique_ids'] = create_int_feature([feature.unique_id])
        features['input_ids'] = create_int_feature(feature.input_ids)
        features['input_mask'] = create_int_feature(feature.input_mask)
        features['segment_ids'] = create_int_feature(feature.segment_ids)
        features['p_mask'] = create_int_feature(feature.p_mask)

        if self.is_training:
            features['start_positions'] = create_int_feature(
                [feature.start_position]
            )
            features['end_positions'] = create_int_feature(
                [feature.end_position]
            )
            impossible = 0
            if feature.is_impossible:
                impossible = 1
            features['is_impossible'] = create_int_feature([impossible])

        tf_example = tf.train.Example(
            features = tf.train.Features(feature = features)
        )
        self._writer.write(tf_example.SerializeToString())

    def close(self):
        self._writer.close()


def input_fn_builder(
    input_file, seq_length, is_training, drop_remainder, use_tpu, bsz, is_v2
):
    """Creates an `input_fn` closure to be passed to TPUEstimator."""

    name_to_features = {
        'unique_ids': tf.FixedLenFeature([], tf.int64),
        'input_ids': tf.FixedLenFeature([seq_length], tf.int64),
        'input_mask': tf.FixedLenFeature([seq_length], tf.int64),
        'segment_ids': tf.FixedLenFeature([seq_length], tf.int64),
    }
    # p_mask is not required for SQuAD v1.1
    if is_v2:
        name_to_features['p_mask'] = tf.FixedLenFeature([seq_length], tf.int64)

    if is_training:
        name_to_features['start_positions'] = tf.FixedLenFeature([], tf.int64)
        name_to_features['end_positions'] = tf.FixedLenFeature([], tf.int64)
        name_to_features['is_impossible'] = tf.FixedLenFeature([], tf.int64)

    def _decode_record(record, name_to_features):
        """Decodes a record to a TensorFlow example."""
        example = tf.parse_single_example(record, name_to_features)

        # tf.Example only supports tf.int64, but the TPU only supports tf.int32.
        # So cast all int64 to int32.
        for name in list(example.keys()):
            t = example[name]
            if t.dtype == tf.int64:
                t = tf.to_int32(t)
            example[name] = t

        return example

    def input_fn(params):
        """The actual input function."""
        if use_tpu:
            batch_size = params['batch_size']
        else:
            batch_size = bsz

        # For training, we want a lot of parallel reading and shuffling.
        # For eval, we want no shuffling and parallel reading doesn't matter.
        d = tf.data.TFRecordDataset(input_file)
        if is_training:
            d = d.repeat()
            d = d.shuffle(buffer_size = 100)

        d = d.apply(
            contrib_data.map_and_batch(
                lambda record: _decode_record(record, name_to_features),
                batch_size = batch_size,
                drop_remainder = drop_remainder,
            )
        )

        return d

    return input_fn


# def create_v1_model(
#     albert_config,
#     is_training,
#     input_ids,
#     input_mask,
#     segment_ids,
#     use_one_hot_embeddings,
#     use_einsum,
#     hub_module,
# ):
#     """Creates a classification model."""
#     (_, final_hidden) = fine_tuning_utils.create_albert(
#         albert_config = albert_config,
#         is_training = is_training,
#         input_ids = input_ids,
#         input_mask = input_mask,
#         segment_ids = segment_ids,
#         use_one_hot_embeddings = use_one_hot_embeddings,
#         use_einsum = use_einsum,
#         hub_module = hub_module,
#     )

#     final_hidden_shape = modeling.get_shape_list(
#         final_hidden, expected_rank = 3
#     )
#     batch_size = final_hidden_shape[0]
#     seq_length = final_hidden_shape[1]
#     hidden_size = final_hidden_shape[2]

#     output_weights = tf.get_variable(
#         'cls/squad/output_weights',
#         [2, hidden_size],
#         initializer = tf.truncated_normal_initializer(stddev = 0.02),
#     )

#     output_bias = tf.get_variable(
#         'cls/squad/output_bias', [2], initializer = tf.zeros_initializer()
#     )

#     final_hidden_matrix = tf.reshape(
#         final_hidden, [batch_size * seq_length, hidden_size]
#     )
#     logits = tf.matmul(final_hidden_matrix, output_weights, transpose_b = True)
#     logits = tf.nn.bias_add(logits, output_bias)

#     logits = tf.reshape(logits, [batch_size, seq_length, 2])
#     logits = tf.transpose(logits, [2, 0, 1])

#     unstacked_logits = tf.unstack(logits, axis = 0)

#     (start_logits, end_logits) = (unstacked_logits[0], unstacked_logits[1])

#     return (start_logits, end_logits)


# def v1_model_fn_builder(
#     albert_config,
#     init_checkpoint,
#     learning_rate,
#     num_train_steps,
#     num_warmup_steps,
#     use_tpu,
#     use_one_hot_embeddings,
#     use_einsum,
#     hub_module,
# ):
#     """Returns `model_fn` closure for TPUEstimator."""

#     def model_fn(
#         features, labels, mode, params
#     ):  # pylint: disable=unused-argument
#         """The `model_fn` for TPUEstimator."""

#         tf.logging.info('*** Features ***')
#         for name in sorted(features.keys()):
#             tf.logging.info(
#                 '  name = %s, shape = %s' % (name, features[name].shape)
#             )

#         if 'unique_ids' in features:
#             unique_ids = features['unique_ids']
#         else:
#             unique_ids = None
#         input_ids = features['input_ids']
#         input_mask = features['input_mask']
#         segment_ids = features['segment_ids']

#         is_training = mode == tf.estimator.ModeKeys.TRAIN

#         (start_logits, end_logits) = create_v1_model(
#             albert_config = albert_config,
#             is_training = is_training,
#             input_ids = input_ids,
#             input_mask = input_mask,
#             segment_ids = segment_ids,
#             use_one_hot_embeddings = use_one_hot_embeddings,
#             use_einsum = use_einsum,
#             hub_module = hub_module,
#         )

#         # Assign names to the logits so that we can refer to them as output tensors.
#         start_logits = tf.identity(start_logits, name = 'start_logits')
#         end_logits = tf.identity(end_logits, name = 'end_logits')

#         tvars = tf.trainable_variables()

#         initialized_variable_names = {}
#         scaffold_fn = None
#         if init_checkpoint:
#             (
#                 assignment_map,
#                 initialized_variable_names,
#             ) = modeling.get_assignment_map_from_checkpoint(
#                 tvars, init_checkpoint
#             )
#             if use_tpu:

#                 def tpu_scaffold():
#                     tf.train.init_from_checkpoint(
#                         init_checkpoint, assignment_map
#                     )
#                     return tf.train.Scaffold()

#                 scaffold_fn = tpu_scaffold
#             else:
#                 tf.train.init_from_checkpoint(init_checkpoint, assignment_map)

#         tf.logging.info('**** Trainable Variables ****')
#         for var in tvars:
#             init_string = ''
#             if var.name in initialized_variable_names:
#                 init_string = ', *INIT_FROM_CKPT*'
#             tf.logging.info(
#                 '  name = %s, shape = %s%s', var.name, var.shape, init_string
#             )

#         output_spec = None
#         if mode == tf.estimator.ModeKeys.TRAIN:
#             seq_length = modeling.get_shape_list(input_ids)[1]

#             def compute_loss(logits, positions):
#                 one_hot_positions = tf.one_hot(
#                     positions, depth = seq_length, dtype = tf.float32
#                 )
#                 log_probs = tf.nn.log_softmax(logits, axis = -1)
#                 loss = -tf.reduce_mean(
#                     tf.reduce_sum(one_hot_positions * log_probs, axis = -1)
#                 )
#                 return loss

#             start_positions = features['start_positions']
#             end_positions = features['end_positions']

#             start_loss = compute_loss(start_logits, start_positions)
#             end_loss = compute_loss(end_logits, end_positions)

#             total_loss = (start_loss + end_loss) / 2.0

#             train_op = optimization.create_optimizer(
#                 total_loss,
#                 learning_rate,
#                 num_train_steps,
#                 num_warmup_steps,
#                 use_tpu,
#             )

#             output_spec = contrib_tpu.TPUEstimatorSpec(
#                 mode = mode,
#                 loss = total_loss,
#                 train_op = train_op,
#                 scaffold_fn = scaffold_fn,
#             )
#         elif mode == tf.estimator.ModeKeys.PREDICT:
#             predictions = {
#                 'start_log_prob': start_logits,
#                 'end_log_prob': end_logits,
#             }
#             if unique_ids is not None:
#                 predictions['unique_ids'] = unique_ids
#             output_spec = contrib_tpu.TPUEstimatorSpec(
#                 mode = mode,
#                 predictions = predictions,
#                 scaffold_fn = scaffold_fn,
#             )
#         else:
#             raise ValueError(
#                 'Only TRAIN and PREDICT modes are supported: %s' % (mode)
#             )
#         return output_spec

#     return model_fn


def accumulate_predictions_v1(
    result_dict,
    all_examples,
    all_features,
    all_results,
    n_best_size,
    max_answer_length,
):
    """accumulate predictions for each positions in a dictionary."""
    example_index_to_features = collections.defaultdict(list)
    for feature in all_features:
        example_index_to_features[feature.example_index].append(feature)

    unique_id_to_result = {}
    for result in all_results:
        unique_id_to_result[result.unique_id] = result

    all_predictions = collections.OrderedDict()
    all_nbest_json = collections.OrderedDict()
    scores_diff_json = collections.OrderedDict()

    for (example_index, example) in enumerate(all_examples):
        if example_index not in result_dict:
            result_dict[example_index] = {}
        features = example_index_to_features[example_index]

        prelim_predictions = []
        min_null_feature_index = 0  # the paragraph slice with min mull score
        null_start_logit = 0  # the start logit at the slice with min null score
        null_end_logit = 0  # the end logit at the slice with min null score
        for (feature_index, feature) in enumerate(features):
            if feature.unique_id not in result_dict[example_index]:
                result_dict[example_index][feature.unique_id] = {}
            result = unique_id_to_result[feature.unique_id]
            start_indexes = _get_best_indexes(
                result.start_log_prob, n_best_size
            )
            end_indexes = _get_best_indexes(result.end_log_prob, n_best_size)
            for start_index in start_indexes:
                for end_index in end_indexes:
                    doc_offset = feature.tokens.index('[SEP]') + 1
                    # We could hypothetically create invalid predictions, e.g., predict
                    # that the start of the span is in the question. We throw out all
                    # invalid predictions.
                    if start_index - doc_offset >= len(
                        feature.tok_start_to_orig_index
                    ):
                        continue
                    if end_index - doc_offset >= len(
                        feature.tok_end_to_orig_index
                    ):
                        continue
                    if not feature.token_is_max_context.get(start_index, False):
                        continue
                    if end_index < start_index:
                        continue
                    length = end_index - start_index + 1
                    if length > max_answer_length:
                        continue
                    start_log_prob = result.start_log_prob[start_index]
                    end_log_prob = result.end_log_prob[end_index]
                    start_idx = start_index - doc_offset
                    end_idx = end_index - doc_offset
                    if (start_idx, end_idx) not in result_dict[example_index][
                        feature.unique_id
                    ]:
                        result_dict[example_index][feature.unique_id][
                            (start_idx, end_idx)
                        ] = []
                    result_dict[example_index][feature.unique_id][
                        (start_idx, end_idx)
                    ].append((start_log_prob, end_log_prob))


def write_predictions_v1(
    result_dict,
    all_examples,
    all_features,
    all_results,
    n_best_size,
    max_answer_length,
    output_prediction_file,
    output_nbest_file,
):
    """Write final predictions to the json file and log-odds of null if needed."""
    tf.logging.info('Writing predictions to: %s' % (output_prediction_file))
    tf.logging.info('Writing nbest to: %s' % (output_nbest_file))

    example_index_to_features = collections.defaultdict(list)
    for feature in all_features:
        example_index_to_features[feature.example_index].append(feature)

    unique_id_to_result = {}
    for result in all_results:
        unique_id_to_result[result.unique_id] = result

    all_predictions = collections.OrderedDict()
    all_nbest_json = collections.OrderedDict()
    scores_diff_json = collections.OrderedDict()

    for (example_index, example) in enumerate(all_examples):
        features = example_index_to_features[example_index]

        prelim_predictions = []
        # keep track of the minimum score of null start+end of position 0
        score_null = 1000000  # large and positive
        min_null_feature_index = 0  # the paragraph slice with min mull score
        null_start_logit = 0  # the start logit at the slice with min null score
        null_end_logit = 0  # the end logit at the slice with min null score
        for (feature_index, feature) in enumerate(features):
            for ((start_idx, end_idx), logprobs) in result_dict[example_index][
                feature.unique_id
            ].items():
                start_log_prob = 0
                end_log_prob = 0
                for logprob in logprobs:
                    start_log_prob += logprob[0]
                    end_log_prob += logprob[1]
                prelim_predictions.append(
                    _PrelimPrediction(
                        feature_index = feature_index,
                        start_index = start_idx,
                        end_index = end_idx,
                        start_log_prob = start_log_prob / len(logprobs),
                        end_log_prob = end_log_prob / len(logprobs),
                    )
                )

        prelim_predictions = sorted(
            prelim_predictions,
            key = lambda x: (x.start_log_prob + x.end_log_prob),
            reverse = True,
        )

        seen_predictions = {}
        nbest = []
        for pred in prelim_predictions:
            if len(nbest) >= n_best_size:
                break
            feature = features[pred.feature_index]
            if pred.start_index >= 0:  # this is a non-null prediction
                tok_start_to_orig_index = feature.tok_start_to_orig_index
                tok_end_to_orig_index = feature.tok_end_to_orig_index
                start_orig_pos = tok_start_to_orig_index[pred.start_index]
                end_orig_pos = tok_end_to_orig_index[pred.end_index]

                paragraph_text = example.paragraph_text
                final_text = paragraph_text[
                    start_orig_pos : end_orig_pos + 1
                ].strip()
                if final_text in seen_predictions:
                    continue

                seen_predictions[final_text] = True
            else:
                final_text = ''
                seen_predictions[final_text] = True

            nbest.append(
                _NbestPrediction(
                    text = final_text,
                    start_log_prob = pred.start_log_prob,
                    end_log_prob = pred.end_log_prob,
                )
            )

        # In very rare edge cases we could have no valid predictions. So we
        # just create a nonce prediction in this case to avoid failure.
        if not nbest:
            nbest.append(
                _NbestPrediction(
                    text = 'empty', start_log_prob = 0.0, end_log_prob = 0.0
                )
            )

        assert len(nbest) >= 1

        total_scores = []
        best_non_null_entry = None
        for entry in nbest:
            total_scores.append(entry.start_log_prob + entry.end_log_prob)
            if not best_non_null_entry:
                if entry.text:
                    best_non_null_entry = entry

        probs = _compute_softmax(total_scores)

        nbest_json = []
        for (i, entry) in enumerate(nbest):
            output = collections.OrderedDict()
            output['text'] = entry.text
            output['probability'] = probs[i]
            output['start_log_prob'] = entry.start_log_prob
            output['end_log_prob'] = entry.end_log_prob
            nbest_json.append(output)

        assert len(nbest_json) >= 1

        all_predictions[example.qas_id] = nbest_json[0]['text']
        all_nbest_json[example.qas_id] = nbest_json

    with tf.gfile.GFile(output_prediction_file, 'w') as writer:
        writer.write(json.dumps(all_predictions, indent = 4) + '\n')

    with tf.gfile.GFile(output_nbest_file, 'w') as writer:
        writer.write(json.dumps(all_nbest_json, indent = 4) + '\n')

    return all_predictions


####### following are from official SQuAD v1.1 evaluation scripts
def normalize_answer_v1(s):
    """Lower text and remove punctuation, articles and extra whitespace."""

    def remove_articles(text):
        return re.sub(r'\b(a|an|the)\b', ' ', text)

    def white_space_fix(text):
        return ' '.join(text.split())

    def remove_punc(text):
        exclude = set(string.punctuation)
        return ''.join(ch for ch in text if ch not in exclude)

    def lower(text):
        return text.lower()

    return white_space_fix(remove_articles(remove_punc(lower(s))))


def f1_score(prediction, ground_truth):
    prediction_tokens = normalize_answer_v1(prediction).split()
    ground_truth_tokens = normalize_answer_v1(ground_truth).split()
    common = collections.Counter(prediction_tokens) & collections.Counter(
        ground_truth_tokens
    )
    num_same = sum(common.values())
    if num_same == 0:
        return 0
    precision = 1.0 * num_same / len(prediction_tokens)
    recall = 1.0 * num_same / len(ground_truth_tokens)
    f1 = (2 * precision * recall) / (precision + recall)
    return f1


def exact_match_score(prediction, ground_truth):
    return normalize_answer_v1(prediction) == normalize_answer_v1(ground_truth)


def metric_max_over_ground_truths(metric_fn, prediction, ground_truths):
    scores_for_ground_truths = []
    for ground_truth in ground_truths:
        score = metric_fn(prediction, ground_truth)
        scores_for_ground_truths.append(score)
    return max(scores_for_ground_truths)


def evaluate_v1(dataset, predictions):
    f1 = exact_match = total = 0
    for article in dataset:
        for paragraph in article['paragraphs']:
            for qa in paragraph['qas']:
                total += 1
                if qa['id'] not in predictions:
                    message = (
                        'Unanswered question '
                        + six.ensure_str(qa['id'])
                        + '  will receive score 0.'
                    )
                    print(message, file = sys.stderr)
                    continue
                ground_truths = [x['text'] for x in qa['answers']]
                # ground_truths = list(map(lambda x: x["text"], qa["answers"]))
                prediction = predictions[qa['id']]
                exact_match += metric_max_over_ground_truths(
                    exact_match_score, prediction, ground_truths
                )
                f1 += metric_max_over_ground_truths(
                    f1_score, prediction, ground_truths
                )

    exact_match = 100.0 * exact_match / total
    f1 = 100.0 * f1 / total

    return {'exact_match': exact_match, 'f1': f1}


####### above are from official SQuAD v1.1 evaluation scripts
####### following are from official SQuAD v2.0 evaluation scripts
def make_qid_to_has_ans(dataset):
    qid_to_has_ans = {}
    for article in dataset:
        for p in article['paragraphs']:
            for qa in p['qas']:
                qid_to_has_ans[qa['id']] = bool(qa['answers'])
    return qid_to_has_ans


def normalize_answer_v2(s):
    """Lower text and remove punctuation, articles and extra whitespace."""

    def remove_articles(text):
        regex = re.compile(r'\b(a|an|the)\b', re.UNICODE)
        return re.sub(regex, ' ', text)

    def white_space_fix(text):
        return ' '.join(text.split())

    def remove_punc(text):
        exclude = set(string.punctuation)
        return ''.join(ch for ch in text if ch not in exclude)

    def lower(text):
        return text.lower()

    return white_space_fix(remove_articles(remove_punc(lower(s))))


def get_tokens(s):
    if not s:
        return []
    return normalize_answer_v2(s).split()


def compute_exact(a_gold, a_pred):
    return int(normalize_answer_v2(a_gold) == normalize_answer_v2(a_pred))


def compute_f1(a_gold, a_pred):
    gold_toks = get_tokens(a_gold)
    pred_toks = get_tokens(a_pred)
    common = collections.Counter(gold_toks) & collections.Counter(pred_toks)
    num_same = sum(common.values())
    if len(gold_toks) == 0 or len(pred_toks) == 0:
        # If either is no-answer, then F1 is 1 if they agree, 0 otherwise
        return int(gold_toks == pred_toks)
    if num_same == 0:
        return 0
    precision = 1.0 * num_same / len(pred_toks)
    recall = 1.0 * num_same / len(gold_toks)
    f1 = (2 * precision * recall) / (precision + recall)
    return f1


def get_raw_scores(dataset, preds):
    exact_scores = {}
    f1_scores = {}
    for article in dataset:
        for p in article['paragraphs']:
            for qa in p['qas']:
                qid = qa['id']
                gold_answers = [
                    a['text']
                    for a in qa['answers']
                    if normalize_answer_v2(a['text'])
                ]
                if not gold_answers:
                    # For unanswerable questions, only correct answer is empty string
                    gold_answers = ['']
                if qid not in preds:
                    print('Missing prediction for %s' % qid)
                    continue
                a_pred = preds[qid]
                # Take max over all gold answers
                exact_scores[qid] = max(
                    compute_exact(a, a_pred) for a in gold_answers
                )
                f1_scores[qid] = max(
                    compute_f1(a, a_pred) for a in gold_answers
                )
    return exact_scores, f1_scores


def apply_no_ans_threshold(scores, na_probs, qid_to_has_ans, na_prob_thresh):
    new_scores = {}
    for qid, s in scores.items():
        pred_na = na_probs[qid] > na_prob_thresh
        if pred_na:
            new_scores[qid] = float(not qid_to_has_ans[qid])
        else:
            new_scores[qid] = s
    return new_scores


def make_eval_dict(exact_scores, f1_scores, qid_list = None):
    if not qid_list:
        total = len(exact_scores)
        return collections.OrderedDict(
            [
                ('exact', 100.0 * sum(exact_scores.values()) / total),
                ('f1', 100.0 * sum(f1_scores.values()) / total),
                ('total', total),
            ]
        )
    else:
        total = len(qid_list)
        return collections.OrderedDict(
            [
                (
                    'exact',
                    100.0 * sum(exact_scores[k] for k in qid_list) / total,
                ),
                ('f1', 100.0 * sum(f1_scores[k] for k in qid_list) / total),
                ('total', total),
            ]
        )


def find_best_thresh(preds, scores, na_probs, qid_to_has_ans):
    num_no_ans = sum(1 for k in qid_to_has_ans if not qid_to_has_ans[k])
    cur_score = num_no_ans
    best_score = cur_score
    best_thresh = 0.0
    qid_list = sorted(na_probs, key = lambda k: na_probs[k])
    for i, qid in enumerate(qid_list):
        if qid not in scores:
            continue
        if qid_to_has_ans[qid]:
            diff = scores[qid]
        else:
            if preds[qid]:
                diff = -1
            else:
                diff = 0
        cur_score += diff
        if cur_score > best_score:
            best_score = cur_score
            best_thresh = na_probs[qid]
    return 100.0 * best_score / len(scores), best_thresh


def find_all_best_thresh(
    main_eval, preds, exact_raw, f1_raw, na_probs, qid_to_has_ans
):
    best_exact, exact_thresh = find_best_thresh(
        preds, exact_raw, na_probs, qid_to_has_ans
    )
    best_f1, f1_thresh = find_best_thresh(
        preds, f1_raw, na_probs, qid_to_has_ans
    )
    main_eval['best_exact'] = best_exact
    main_eval['best_exact_thresh'] = exact_thresh
    main_eval['best_f1'] = best_f1
    main_eval['best_f1_thresh'] = f1_thresh


def merge_eval(main_eval, new_eval, prefix):
    for k in new_eval:
        main_eval['%s_%s' % (prefix, k)] = new_eval[k]


####### above are from official SQuAD v2.0 evaluation scripts


def accumulate_predictions_v2(
    result_dict,
    cls_dict,
    all_examples,
    all_features,
    all_results,
    n_best_size,
    max_answer_length,
    start_n_top,
    end_n_top,
):
    """accumulate predictions for each positions in a dictionary."""

    example_index_to_features = collections.defaultdict(list)
    for feature in all_features:
        example_index_to_features[feature.example_index].append(feature)

    unique_id_to_result = {}
    for result in all_results:
        unique_id_to_result[result.unique_id] = result

    all_predictions = collections.OrderedDict()
    all_nbest_json = collections.OrderedDict()
    scores_diff_json = collections.OrderedDict()

    for (example_index, example) in enumerate(all_examples):
        if example_index not in result_dict:
            result_dict[example_index] = {}
        features = example_index_to_features[example_index]

        prelim_predictions = []
        # keep track of the minimum score of null start+end of position 0
        score_null = 1000000  # large and positive

        for (feature_index, feature) in enumerate(features):
            if feature.unique_id not in result_dict[example_index]:
                result_dict[example_index][feature.unique_id] = {}
            result = unique_id_to_result[feature.unique_id]
            cur_null_score = result.cls_logits

            # if we could have irrelevant answers, get the min score of irrelevant
            score_null = min(score_null, cur_null_score)

            doc_offset = feature.tokens.index('[SEP]') + 1
            for i in range(start_n_top):
                for j in range(end_n_top):
                    start_log_prob = result.start_top_log_probs[i]
                    start_index = result.start_top_index[i]

                    j_index = i * end_n_top + j

                    end_log_prob = result.end_top_log_probs[j_index]
                    end_index = result.end_top_index[j_index]
                    # We could hypothetically create invalid predictions, e.g., predict
                    # that the start of the span is in the question. We throw out all
                    # invalid predictions.
                    if start_index - doc_offset >= len(
                        feature.tok_start_to_orig_index
                    ):
                        continue
                    if start_index - doc_offset < 0:
                        continue
                    if end_index - doc_offset >= len(
                        feature.tok_end_to_orig_index
                    ):
                        continue
                    if not feature.token_is_max_context.get(start_index, False):
                        continue
                    if end_index < start_index:
                        continue
                    length = end_index - start_index + 1
                    if length > max_answer_length:
                        continue
                    start_idx = start_index - doc_offset
                    end_idx = end_index - doc_offset
                    if (start_idx, end_idx) not in result_dict[example_index][
                        feature.unique_id
                    ]:
                        result_dict[example_index][feature.unique_id][
                            (start_idx, end_idx)
                        ] = []
                    result_dict[example_index][feature.unique_id][
                        (start_idx, end_idx)
                    ].append((start_log_prob, end_log_prob))
        if example_index not in cls_dict:
            cls_dict[example_index] = []
        cls_dict[example_index].append(score_null)


def write_predictions_v2(
    result_dict,
    cls_dict,
    all_examples,
    all_features,
    all_results,
    n_best_size,
    max_answer_length,
    output_prediction_file,
    output_nbest_file,
    output_null_log_odds_file,
    null_score_diff_threshold,
):
    """Write final predictions to the json file and log-odds of null if needed."""
    tf.logging.info('Writing predictions to: %s' % (output_prediction_file))
    tf.logging.info('Writing nbest to: %s' % (output_nbest_file))

    example_index_to_features = collections.defaultdict(list)
    for feature in all_features:
        example_index_to_features[feature.example_index].append(feature)

    unique_id_to_result = {}
    for result in all_results:
        unique_id_to_result[result.unique_id] = result

    all_predictions = collections.OrderedDict()
    all_nbest_json = collections.OrderedDict()
    scores_diff_json = collections.OrderedDict()

    for (example_index, example) in enumerate(all_examples):
        features = example_index_to_features[example_index]

        prelim_predictions = []
        # keep track of the minimum score of null start+end of position 0
        # score_null = 1000000  # large and positive

        for (feature_index, feature) in enumerate(features):
            for ((start_idx, end_idx), logprobs) in result_dict[example_index][
                feature.unique_id
            ].items():
                start_log_prob = 0
                end_log_prob = 0
                for logprob in logprobs:
                    start_log_prob += logprob[0]
                    end_log_prob += logprob[1]
                prelim_predictions.append(
                    _PrelimPrediction(
                        feature_index = feature_index,
                        start_index = start_idx,
                        end_index = end_idx,
                        start_log_prob = start_log_prob / len(logprobs),
                        end_log_prob = end_log_prob / len(logprobs),
                    )
                )

        prelim_predictions = sorted(
            prelim_predictions,
            key = lambda x: (x.start_log_prob + x.end_log_prob),
            reverse = True,
        )

        seen_predictions = {}
        nbest = []
        for pred in prelim_predictions:
            if len(nbest) >= n_best_size:
                break
            feature = features[pred.feature_index]

            tok_start_to_orig_index = feature.tok_start_to_orig_index
            tok_end_to_orig_index = feature.tok_end_to_orig_index
            start_orig_pos = tok_start_to_orig_index[pred.start_index]
            end_orig_pos = tok_end_to_orig_index[pred.end_index]

            paragraph_text = example.paragraph_text
            final_text = paragraph_text[
                start_orig_pos : end_orig_pos + 1
            ].strip()

            if final_text in seen_predictions:
                continue

            seen_predictions[final_text] = True

            nbest.append(
                _NbestPrediction(
                    text = final_text,
                    start_log_prob = pred.start_log_prob,
                    end_log_prob = pred.end_log_prob,
                )
            )

        # In very rare edge cases we could have no valid predictions. So we
        # just create a nonce prediction in this case to avoid failure.
        if not nbest:
            nbest.append(
                _NbestPrediction(
                    text = '', start_log_prob = -1e6, end_log_prob = -1e6
                )
            )

        total_scores = []
        best_non_null_entry = None
        for entry in nbest:
            total_scores.append(entry.start_log_prob + entry.end_log_prob)
            if not best_non_null_entry:
                best_non_null_entry = entry

        probs = _compute_softmax(total_scores)

        nbest_json = []
        for (i, entry) in enumerate(nbest):
            output = collections.OrderedDict()
            output['text'] = entry.text
            output['probability'] = probs[i]
            output['start_log_prob'] = entry.start_log_prob
            output['end_log_prob'] = entry.end_log_prob
            nbest_json.append(output)

        assert len(nbest_json) >= 1
        assert best_non_null_entry is not None

        score_diff = sum(cls_dict[example_index]) / len(cls_dict[example_index])
        scores_diff_json[example.qas_id] = score_diff
        # predict null answers when null threshold is provided
        if (
            null_score_diff_threshold is None
            or score_diff < null_score_diff_threshold
        ):
            all_predictions[example.qas_id] = best_non_null_entry.text
        else:
            all_predictions[example.qas_id] = ''

        all_nbest_json[example.qas_id] = nbest_json
        assert len(nbest_json) >= 1

    with tf.gfile.GFile(output_prediction_file, 'w') as writer:
        writer.write(json.dumps(all_predictions, indent = 4) + '\n')

    with tf.gfile.GFile(output_nbest_file, 'w') as writer:
        writer.write(json.dumps(all_nbest_json, indent = 4) + '\n')

    with tf.gfile.GFile(output_null_log_odds_file, 'w') as writer:
        writer.write(json.dumps(scores_diff_json, indent = 4) + '\n')
    return all_predictions, scores_diff_json


# def create_v2_model(
#     albert_config,
#     is_training,
#     input_ids,
#     input_mask,
#     segment_ids,
#     use_one_hot_embeddings,
#     features,
#     max_seq_length,
#     start_n_top,
#     end_n_top,
#     dropout_prob,
#     hub_module,
# ):
#     """Creates a classification model."""
#     (_, output) = fine_tuning_utils.create_albert(
#         albert_config = albert_config,
#         is_training = is_training,
#         input_ids = input_ids,
#         input_mask = input_mask,
#         segment_ids = segment_ids,
#         use_one_hot_embeddings = use_one_hot_embeddings,
#         use_einsum = True,
#         hub_module = hub_module,
#     )

#     bsz = tf.shape(output)[0]
#     return_dict = {}
#     output = tf.transpose(output, [1, 0, 2])

#     # invalid position mask such as query and special symbols (PAD, SEP, CLS)
#     p_mask = tf.cast(features['p_mask'], dtype = tf.float32)

#     # logit of the start position
#     with tf.variable_scope('start_logits'):
#         start_logits = tf.layers.dense(
#             output,
#             1,
#             kernel_initializer = modeling.create_initializer(
#                 albert_config.initializer_range
#             ),
#         )
#         start_logits = tf.transpose(tf.squeeze(start_logits, -1), [1, 0])
#         start_logits_masked = start_logits * (1 - p_mask) - 1e30 * p_mask
#         start_log_probs = tf.nn.log_softmax(start_logits_masked, -1)

#     # logit of the end position
#     with tf.variable_scope('end_logits'):
#         if is_training:
#             # during training, compute the end logits based on the
#             # ground truth of the start position
#             start_positions = tf.reshape(features['start_positions'], [-1])
#             start_index = tf.one_hot(
#                 start_positions,
#                 depth = max_seq_length,
#                 axis = -1,
#                 dtype = tf.float32,
#             )
#             start_features = tf.einsum('lbh,bl->bh', output, start_index)
#             start_features = tf.tile(
#                 start_features[None], [max_seq_length, 1, 1]
#             )
#             end_logits = tf.layers.dense(
#                 tf.concat([output, start_features], axis = -1),
#                 albert_config.hidden_size,
#                 kernel_initializer = modeling.create_initializer(
#                     albert_config.initializer_range
#                 ),
#                 activation = tf.tanh,
#                 name = 'dense_0',
#             )
#             end_logits = contrib_layers.layer_norm(
#                 end_logits, begin_norm_axis = -1
#             )

#             end_logits = tf.layers.dense(
#                 end_logits,
#                 1,
#                 kernel_initializer = modeling.create_initializer(
#                     albert_config.initializer_range
#                 ),
#                 name = 'dense_1',
#             )
#             end_logits = tf.transpose(tf.squeeze(end_logits, -1), [1, 0])
#             end_logits_masked = end_logits * (1 - p_mask) - 1e30 * p_mask
#             end_log_probs = tf.nn.log_softmax(end_logits_masked, -1)
#         else:
#             # during inference, compute the end logits based on beam search

#             start_top_log_probs, start_top_index = tf.nn.top_k(
#                 start_log_probs, k = start_n_top
#             )
#             start_index = tf.one_hot(
#                 start_top_index,
#                 depth = max_seq_length,
#                 axis = -1,
#                 dtype = tf.float32,
#             )
#             start_features = tf.einsum('lbh,bkl->bkh', output, start_index)
#             end_input = tf.tile(output[:, :, None], [1, 1, start_n_top, 1])
#             start_features = tf.tile(
#                 start_features[None], [max_seq_length, 1, 1, 1]
#             )
#             end_input = tf.concat([end_input, start_features], axis = -1)
#             end_logits = tf.layers.dense(
#                 end_input,
#                 albert_config.hidden_size,
#                 kernel_initializer = modeling.create_initializer(
#                     albert_config.initializer_range
#                 ),
#                 activation = tf.tanh,
#                 name = 'dense_0',
#             )
#             end_logits = contrib_layers.layer_norm(
#                 end_logits, begin_norm_axis = -1
#             )
#             end_logits = tf.layers.dense(
#                 end_logits,
#                 1,
#                 kernel_initializer = modeling.create_initializer(
#                     albert_config.initializer_range
#                 ),
#                 name = 'dense_1',
#             )
#             end_logits = tf.reshape(
#                 end_logits, [max_seq_length, -1, start_n_top]
#             )
#             end_logits = tf.transpose(end_logits, [1, 2, 0])
#             end_logits_masked = (
#                 end_logits * (1 - p_mask[:, None]) - 1e30 * p_mask[:, None]
#             )
#             end_log_probs = tf.nn.log_softmax(end_logits_masked, -1)
#             end_top_log_probs, end_top_index = tf.nn.top_k(
#                 end_log_probs, k = end_n_top
#             )
#             end_top_log_probs = tf.reshape(
#                 end_top_log_probs, [-1, start_n_top * end_n_top]
#             )
#             end_top_index = tf.reshape(
#                 end_top_index, [-1, start_n_top * end_n_top]
#             )

#     if is_training:
#         return_dict['start_log_probs'] = start_log_probs
#         return_dict['end_log_probs'] = end_log_probs
#     else:
#         return_dict['start_top_log_probs'] = start_top_log_probs
#         return_dict['start_top_index'] = start_top_index
#         return_dict['end_top_log_probs'] = end_top_log_probs
#         return_dict['end_top_index'] = end_top_index

#     # an additional layer to predict answerability
#     with tf.variable_scope('answer_class'):
#         # get the representation of CLS
#         cls_index = tf.one_hot(
#             tf.zeros([bsz], dtype = tf.int32),
#             max_seq_length,
#             axis = -1,
#             dtype = tf.float32,
#         )
#         cls_feature = tf.einsum('lbh,bl->bh', output, cls_index)

#         # get the representation of START
#         start_p = tf.nn.softmax(
#             start_logits_masked, axis = -1, name = 'softmax_start'
#         )
#         start_feature = tf.einsum('lbh,bl->bh', output, start_p)

#         # note(zhiliny): no dependency on end_feature so that we can obtain
#         # one single `cls_logits` for each sample
#         ans_feature = tf.concat([start_feature, cls_feature], -1)
#         ans_feature = tf.layers.dense(
#             ans_feature,
#             albert_config.hidden_size,
#             activation = tf.tanh,
#             kernel_initializer = modeling.create_initializer(
#                 albert_config.initializer_range
#             ),
#             name = 'dense_0',
#         )
#         ans_feature = tf.layers.dropout(
#             ans_feature, dropout_prob, training = is_training
#         )
#         cls_logits = tf.layers.dense(
#             ans_feature,
#             1,
#             kernel_initializer = modeling.create_initializer(
#                 albert_config.initializer_range
#             ),
#             name = 'dense_1',
#             use_bias = False,
#         )
#         cls_logits = tf.squeeze(cls_logits, -1)

#         return_dict['cls_logits'] = cls_logits

#     return return_dict


# def v2_model_fn_builder(
#     albert_config,
#     init_checkpoint,
#     learning_rate,
#     num_train_steps,
#     num_warmup_steps,
#     use_tpu,
#     use_one_hot_embeddings,
#     max_seq_length,
#     start_n_top,
#     end_n_top,
#     dropout_prob,
#     hub_module,
# ):
#     """Returns `model_fn` closure for TPUEstimator."""

#     def model_fn(
#         features, labels, mode, params
#     ):  # pylint: disable=unused-argument
#         """The `model_fn` for TPUEstimator."""

#         tf.logging.info('*** Features ***')
#         for name in sorted(features.keys()):
#             tf.logging.info(
#                 '  name = %s, shape = %s' % (name, features[name].shape)
#             )

#         # unique_ids = features["unique_ids"]
#         input_ids = features['input_ids']
#         input_mask = features['input_mask']
#         segment_ids = features['segment_ids']

#         is_training = mode == tf.estimator.ModeKeys.TRAIN

#         outputs = create_v2_model(
#             albert_config = albert_config,
#             is_training = is_training,
#             input_ids = input_ids,
#             input_mask = input_mask,
#             segment_ids = segment_ids,
#             use_one_hot_embeddings = use_one_hot_embeddings,
#             features = features,
#             max_seq_length = max_seq_length,
#             start_n_top = start_n_top,
#             end_n_top = end_n_top,
#             dropout_prob = dropout_prob,
#             hub_module = hub_module,
#         )

#         tvars = tf.trainable_variables()

#         initialized_variable_names = {}
#         scaffold_fn = None
#         if init_checkpoint:
#             (
#                 assignment_map,
#                 initialized_variable_names,
#             ) = modeling.get_assignment_map_from_checkpoint(
#                 tvars, init_checkpoint
#             )
#             if use_tpu:

#                 def tpu_scaffold():
#                     tf.train.init_from_checkpoint(
#                         init_checkpoint, assignment_map
#                     )
#                     return tf.train.Scaffold()

#                 scaffold_fn = tpu_scaffold
#             else:
#                 tf.train.init_from_checkpoint(init_checkpoint, assignment_map)

#         tf.logging.info('**** Trainable Variables ****')
#         for var in tvars:
#             init_string = ''
#             if var.name in initialized_variable_names:
#                 init_string = ', *INIT_FROM_CKPT*'
#             tf.logging.info(
#                 '  name = %s, shape = %s%s', var.name, var.shape, init_string
#             )

#         output_spec = None
#         if mode == tf.estimator.ModeKeys.TRAIN:
#             seq_length = modeling.get_shape_list(input_ids)[1]

#             def compute_loss(log_probs, positions):
#                 one_hot_positions = tf.one_hot(
#                     positions, depth = seq_length, dtype = tf.float32
#                 )

#                 loss = -tf.reduce_sum(one_hot_positions * log_probs, axis = -1)
#                 loss = tf.reduce_mean(loss)
#                 return loss

#             start_loss = compute_loss(
#                 outputs['start_log_probs'], features['start_positions']
#             )
#             end_loss = compute_loss(
#                 outputs['end_log_probs'], features['end_positions']
#             )

#             total_loss = (start_loss + end_loss) * 0.5

#             cls_logits = outputs['cls_logits']
#             is_impossible = tf.reshape(features['is_impossible'], [-1])
#             regression_loss = tf.nn.sigmoid_cross_entropy_with_logits(
#                 labels = tf.cast(is_impossible, dtype = tf.float32),
#                 logits = cls_logits,
#             )
#             regression_loss = tf.reduce_mean(regression_loss)

#             # note(zhiliny): by default multiply the loss by 0.5 so that the scale is
#             # comparable to start_loss and end_loss
#             total_loss += regression_loss * 0.5
#             train_op = optimization.create_optimizer(
#                 total_loss,
#                 learning_rate,
#                 num_train_steps,
#                 num_warmup_steps,
#                 use_tpu,
#             )

#             output_spec = contrib_tpu.TPUEstimatorSpec(
#                 mode = mode,
#                 loss = total_loss,
#                 train_op = train_op,
#                 scaffold_fn = scaffold_fn,
#             )
#         elif mode == tf.estimator.ModeKeys.PREDICT:
#             predictions = {
#                 'unique_ids': features['unique_ids'],
#                 'start_top_index': outputs['start_top_index'],
#                 'start_top_log_probs': outputs['start_top_log_probs'],
#                 'end_top_index': outputs['end_top_index'],
#                 'end_top_log_probs': outputs['end_top_log_probs'],
#                 'cls_logits': outputs['cls_logits'],
#             }
#             output_spec = contrib_tpu.TPUEstimatorSpec(
#                 mode = mode,
#                 predictions = predictions,
#                 scaffold_fn = scaffold_fn,
#             )
#         else:
#             raise ValueError(
#                 'Only TRAIN and PREDICT modes are supported: %s' % (mode)
#             )

#         return output_spec

#     return model_fn


def evaluate_v2(
    result_dict,
    cls_dict,
    prediction_json,
    eval_examples,
    eval_features,
    all_results,
    n_best_size,
    max_answer_length,
    output_prediction_file,
    output_nbest_file,
    output_null_log_odds_file,
):
    null_score_diff_threshold = None
    predictions, na_probs = write_predictions_v2(
        result_dict,
        cls_dict,
        eval_examples,
        eval_features,
        all_results,
        n_best_size,
        max_answer_length,
        output_prediction_file,
        output_nbest_file,
        output_null_log_odds_file,
        null_score_diff_threshold,
    )

    na_prob_thresh = 1.0  # default value taken from the eval script
    qid_to_has_ans = make_qid_to_has_ans(
        prediction_json
    )  # maps qid to True/False
    has_ans_qids = [k for k, v in qid_to_has_ans.items() if v]
    no_ans_qids = [k for k, v in qid_to_has_ans.items() if not v]
    exact_raw, f1_raw = get_raw_scores(prediction_json, predictions)
    exact_thresh = apply_no_ans_threshold(
        exact_raw, na_probs, qid_to_has_ans, na_prob_thresh
    )
    f1_thresh = apply_no_ans_threshold(
        f1_raw, na_probs, qid_to_has_ans, na_prob_thresh
    )
    out_eval = make_eval_dict(exact_thresh, f1_thresh)
    find_all_best_thresh(
        out_eval, predictions, exact_raw, f1_raw, na_probs, qid_to_has_ans
    )
    null_score_diff_threshold = out_eval['best_f1_thresh']

    predictions, na_probs = write_predictions_v2(
        result_dict,
        cls_dict,
        eval_examples,
        eval_features,
        all_results,
        n_best_size,
        max_answer_length,
        output_prediction_file,
        output_nbest_file,
        output_null_log_odds_file,
        null_score_diff_threshold,
    )

    qid_to_has_ans = make_qid_to_has_ans(
        prediction_json
    )  # maps qid to True/False
    has_ans_qids = [k for k, v in qid_to_has_ans.items() if v]
    no_ans_qids = [k for k, v in qid_to_has_ans.items() if not v]
    exact_raw, f1_raw = get_raw_scores(prediction_json, predictions)
    exact_thresh = apply_no_ans_threshold(
        exact_raw, na_probs, qid_to_has_ans, na_prob_thresh
    )
    f1_thresh = apply_no_ans_threshold(
        f1_raw, na_probs, qid_to_has_ans, na_prob_thresh
    )
    out_eval = make_eval_dict(exact_thresh, f1_thresh)
    out_eval['null_score_diff_threshold'] = null_score_diff_threshold
    return out_eval
